A novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense for batch processes

Abstract

Although linear quadratic iterative learning control methods based on nonminimal state space (NMSS) models can directly observe the system state without designing a state observer, there is still room for improvement in their control performance. In order to further improve the control performance of core process indicators for batch processes, this paper presents a novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense (2D‐IHLQILC). Firstly, an extended nonminimal state space (2D‐ENMSS) model in two‐dimensional sense is developed to describe a batch process by incorporating process inputs, outputs, and tracking errors. Secondly, a 2D‐IHLQILC scheme is designed based on this 2D‐ENMSS process model. Finally, the effectiveness of the 2D‐IHLQILC scheme is demonstrated by the injection speed control in injection modeling process. Compared with conventional methods based on NMSS model, the proposed 2D‐IHLQILC method provides more extra degrees of freedom (DOF) to adjust the control performance and acquires improved control performance. In addition, it does not need a state observer to observe the system state.